This invention relates generally to positron emission tomography (PET) systems, and more particularly to performing scatter correction in time-of-flight (TOF) PET systems.
A PET system typically includes a detector ring assembly having rings of detectors that encircle a patient or an object. The detectors detect photons emitted from an annihilation point in the object. Only photons that are detected by two detectors within a predefined time gap are recorded as coincident photons. The two detectors, together, are called a detector pair. When a detector pair detects coincident photons it is assumed that they arose from an annihilation point on the line between the two detectors. When a number of such events are acquired, one of a number of known reconstruction algorithms is applied to estimate the distribution of annihilations within the patient.
A photon generated by an annihilation event may scatter after collision with an atom present in the object or in the environment. If a detector pair detects coincident photons after one or both of the photons scatters, the event is referred to as a scatter coincidence. In a scatter coincidence, the observed line of response is different from the actual path of the unscattered photons. Thus, in order to reconstruct an accurate image of the object being scanned, the acquired data must be corrected for scatter coincidences.
To address the problem of scatter coincidence, Model-Based Scatter Correction (MBSC) methods are known. The inputs to MBSC algorithms are typically an estimate of the emission activity distribution and the attenuation map of the object. The MBSC algorithm estimates the probability distribution of singly scattered coincidences that are the coincidences in which exactly one of the coincident photon scatters. For estimating this probability distribution, the algorithm typically uses the Klein-Nishina equation. Further, the contribution of multiple scattered coincidences is estimated from the single scatter estimate. Multiple scatter coincidences are those in which either both coincident photons scatter or one photon scatters at least twice.
A 3D MBSC algorithm is also known. The algorithm loops through all permutations of an unscattered photon (e.g., over an angle from 0 to 2π) and over all values of radius that intersect the imaged object. In the algorithm, for each unscattered photon detected by a first detector, the probability of scatter at every point (the point termed as scatter point) on the line joining the annihilation point and first detector in the body is calculated. Also, the probability of detecting a scattered photon by a second detector also is calculated. The probability is calculated for each second detector within the field-of-view of the first detector in the detector ring of the PET system. The number of annihilation events recorded by the detector pair is then incremented by a value proportional to the product of the determined probabilities to obtain an estimate of single coincidence. Multiple coincidences are estimated on the basis of the estimate of single coincidences.
It is also known that a PET system can be improved by incorporating TOF information into the data acquisition and reconstruction. In TOF acquisition, each coincidence event is recorded with the difference between the photon detection times in the detector pair. If that difference is measured to be □t, it is inferred that the annihilation event occurred at a distance approximately □t*(c/2) from the midpoint of the line between the two detectors, where c is the speed of light. TOF reconstruction algorithms are known which can utilize this information and produce an image that has less statistical noise than an image acquired and reconstructed without TOF information. These algorithms, however, will produce inaccurate images of the annihilation distribution if the input data is not corrected for the occurrence of scatter coincidences in the TOF data.
There are several known methods for scatter correction that estimate only the number of scatter coincidences in a detector pair, without regard to any TOF information. For example, a TOF scatter correction technique is known and that uses a simple scaling of the non-TOF scatter estimate for the various TOF data elements. However, using only a count of the number of events, or a simple scaling technique, may result in less than acceptable image resolution, particularly when imaging smaller objects.